A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data

Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness, and growing season length) often termed “land surface phenology,” as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data-processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multiscale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams, and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization

Reviews and syntheses:Remotely sensed optical time series for monitoring vegetation productivity

International audienceAbstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring

Changing landscapes: Compositional and phenological shifts in New Zealand's natural grassland

Vegetation in a wide range of ecosystems across the globe is responding to recent anthropogenic climate change. There are two key ecological responses in plants associated with recent anthropogenic climate change: shifts in species’ geographic distributions (range shifts) and shifts in the timing of key life cycle events (phenological shifts). These shifts can lead to temporal and spatial changes in vegetation composition and growth activity and hence ecosystem function. Understanding the patterns and processes of these shifts is crucial for the successful management of natural ecosystems under ongoing anthropogenic environmental change. This thesis investigates recent spatiotemporal compositional and phenological shifts in New Zealand’s natural grassland ecosystems and identifies potential topographical and climatic drivers of these shifts. Three grassland types in New Zealand are investigated (Alpine, Tall Tussock and Low Producing grasslands). They are characterised by high levels of indigenous endemic plant biodiversity and cover a wide elevation range. This thesis primarily utilises remote sensing information for quantifying growth dynamics and vegetation patterns in these grasslands over the last 16 years and across large spatial scales, i.e., the catchment of the river Clutha/Mata-Au River in South Island, New Zealand. Shrub encroachment in grassland ecosystems is a globally observed example of compositional shifts in ecosystems associated with recent anthropogenic climate change. In New Zealand, where extensive area of current grassland habitats exist because of anthropogenic deforestation, shrub encroachment into grasslands has two distinct facets: firstly the invasion of non-native shrub species into native grasslands (i.e., exotic shrub invasion) and secondly the dispersal of native woody and shrub species into native grasslands (i.e., native shrub recovery). Propagule pressure is a measurement of species’ seed source size in neighbourhood of a focal area, and it is a key determinant of the degree to which a location gets colonised by individuals from species present in the neighbourhood. The spatial patterns of potential native and exotic shrub propagule pressure on three grassland types in New Zealand were quantified with the assumption that proximity of higher shrub coverage indicates higher shrub propagule availability. Results show that Alpine grasslands are mostly surrounded by native shrublands, while Low producing grassland are most at risk from exotic shrub invasion from neighbouring areas. High native and exotic shrub propagule pressure does not generally coincide spatially, however, it occurs in very similar climates for Low Producing grassland but not for Alpine and Tall Tussock grassland. The analysis of recent shrub encroachment over the last five years in a tussock grassland area in the central South Island showed a 0.35% year-1 increase in shrub cover in grassland area located in immediate neighbourhood of shrub. Shrub encroachment speed was strongly correlated with shrub cover in the neighbourhood. Recent shrub encroachment into grasslands was most pronounced in areas with neighbouring shrub cover of greater than 40%. A wide range of species and ecosystems worldwide have shown changes in the timing of life cycle events and growing seasons in a direction congruent with recent anthropogenic climate changes. In this study, temporal trends over the last 16 years in the start, peak and end dates of the growing season were analysed using remotely sensed data on the Normalised Difference Vegetation Index (NDVI) in New Zealand’s three main grassland types. Overall, 90% of Alpine, 86% of Tall Tussock and 89% of Low Producing grassland areas showed an advancing start of the growing season over the last 16 years. In these areas start of the growing advanced by 7.2, 6.0 and 8.8 days per decade in Alpine, Tall Tussock and Low Producing grassland, respectively. Only small changes in timing of the end of the growing season were observed in the three grassland types. The length of growing season extended by 3.2, 5.2 and 7.1 days per decade in three grassland types. Landscape topography (elevation and aspect) played an important role in particular in alpine grasslands: the start of the growing season was strongly correlated with elevation (later start with increasing elevation), while the end of the growing season was strongly correlated with aspect (later end of season on more south-facing slopes). The start of season was delayed by 7.5, 5.1 and 3.7 days/100 m elevation increase in Alpine, Tall Tussock and Low producing grassland, separately. The end of season was advanced by 1.7 (Alpine), 1.3 (Tall Tussock) and delayed by 0.3 (Low Producing) days/10-degree-south on the slopes in these three grassland types. The results from this thesis show that recent shrub invasion into New Zealand grasslands is highest near shrub areas once a threshold of shrub cover in the neighbourhood is reached. Shrub encroachment was highest at lower elevations and on north-facing slopes. It also highlighted a measurable shift to an earlier start and extended length of the growing season in New Zealand’s main grassland types over the last 16 years, but the magnitude of these shifts showed considerable geographic variation. Importantly, this study has shown a high degree of topographical control on the timing of the growing in New Zealand’s grasslands with elevation and aspect acting differentially on start and end of the growing season. This highlights the importance of landscape heterogeneity and microclimates for ecosystem responses to climate change. This study shows that remotely sensed data can be successfully used to elucidate ecosystem-level shifts in temporal dynamics and spatial patterns of vegetation growth in grassland ecosystems

Scaling Near-Surface Remote Sensing To Calibrate And Validate Satellite Monitoring Of Grassland Phenology

Phenology across the U.S. Great Plains has been modeled at a variety of field sites and spatial scales. However, combining these spatial scales has never been accomplished before, and has never been done across multiple field locations. We modeled phenocam Vegetation Indices (VIs) across the Great Plains Region. We used coupled satellite imagery that has been aligned spectrally, for each imagery band to align with one another across the phenocam locations. With this we predicted the phenocam VIs for each year over the six locations.Using our method of coupling the phenocam VIs and the meteorological data we predicted 38 years of phenocam VIs. This resulted in a coupled dataset for each phenocam site across the four VIs. Using the coupled datasets, we were able to predict the phenocam VIs, and examine how they would change over the 38 years of data. While imagery was not available for modeling the 38 years of weather data, we found weather data could act as an acceptable proxy. This means we were able to predict 38 years of VIs using weather data. A main assumption with this method, it that no major changes in the vegetation community took place in the 33 years before the imagery. If a large change did take place, it would be missed because of the data lacking to represent it. Using the phenocam and satellite imagery we were able to predict phenocam GCC, VCI, NDVI, and EVI2 and model them over a five-year period. This modeled six years of phenocam imagery across the Great Plains region and attempted to predict the phenocam VIs for each pixel of the satellite imagery. The primary challenge of this method is aggregating grassland predicted VIs with cropland. This region is dominated by cropland and managed grasslands. In many cases the phenology signal is likely driven by land management decisions, and not purely by vegetation growth characteristics. Future models that take this into account may provide a more accurate model for the region

Monitorização da Evapotranspiração na cultura da vinha. Aplicação do modelo SIMDualKc vs informação derivada de imagens de satélite

Mestrado em Engenharia Agronómica - Engenharia Rural / Instituto Superior de Agronomia. Universidade de LisboaO balanço hídrico do solo em culturas lenhosas é por vezes difícil de calcular com recurso a modelos de estimativa, dado estas culturas terem geralmente um sistema radicular profundante. É comum o sistema radicular destas espécies, nomeadamente em clima mediterrânico, terem uma profundidade maior que a profundidade de controlo dos equipamentos utilizados para realizar medições de teor de água no solo. Neste estudo foram utilizados dados de teor de água no solo obtidos em Pacheco (1989). Estes dados foram medidos numa vinha não regada até uma profundidade de 1,85 metros, o que melhora significamente as condições de utilização de modelos de estimativa do balanço hídrico. A diferença que pode ocorrer entre o volume de solo amostrado e aquele efetivamente explorado pelas plantas pode introduzir erros na modelação. A evapotranspiração da vinha (ETc) foi estimada com recurso aos dados da evapotranspiração de referência e do coeficiente cultural dual. Para este cálculo foram consideradas características da vinha, como a altura e a densidade das plantas que variam ao longo do ciclo cultural anual. Considerou-se estes fatores por serem determinantes na transpiração das plantas e na evaporação a partir do solo. A aplicação desta abordagem (coeficiente cultural dual) foi feita com recurso ao modelo SIMDualKc o que permitiu obter valores estimados dos coeficientes culturais e de ETc. Para obter valores de fração de solo coberto pela cultura e, consequentemente valores de coeficientes culturais foram utilizados dados obtidos através de imagens de satélite. Desta forma foram obtidos valores de coeficiente cultural (Kc) para a vinha em estudo. A calibração do modelo SIMDualKc foi feita através de medições de teor de água no solo com recurso a sonda de neutrões, tendo sido também testado com uma série de dados adicionais. Adicionalmente foi calculado o Kc através de abordagem baseada em índices de vegetação gerados a partir de imagens de satélite e comparados os resultados com os valores de Kc obtidos através do SIMDualKc. Foi possível concluir que há concordância, no ano de estudo, entre os valores de água no solo simulados através do SIMDualKc e medidos em campo. Verificou-se também concordância entre os valores de Kc obtidos com o modelo SIMDualKc e os valores obtidos através da análise de imagens de satélite. A eficiência estatística para a comparação entre os valores de água no solo simulados e medidos foi de 0,93 e o coeficiente de determinação foi de 1,0. A eficiência estatística para a comparação entre o Kc simulado e calculado pela análise de imagens de satélite foi de 0,74 e o coeficiente de determinação foi de 0,98. Ficou assim comprovada a possibilidade de calibração do modelo para estimar a ETc duma vinha não regada, em condições mediterrânicas, utilizando dados do teor de água do solo até elevada profundidadeN/